Showing 4 open source projects for "management"

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  • 1
    clusterProfiler

    clusterProfiler

    A universal enrichment tool for interpreting omics data

    clusterProfiler is an R/Bioconductor package that provides a unified workflow for functional enrichment analysis to interpret high-throughput omics results. It supports both over-representation analysis and gene set enrichment analysis, letting you work with unranked gene lists or ranked statistics from differential pipelines. The package connects to multiple knowledge bases—such as Gene Ontology, KEGG, Reactome, Disease Ontology, MeSH and others—through a consistent interface so you can...
    Downloads: 0 This Week
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  • 2
    LabPlot

    LabPlot

    Data Visualization and Analysis

    LabPlot is a FREE, open source and cross-platform Data Visualization and Analysis software accessible to everyone.
    Downloads: 23 This Week
    Last Update:
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  • 3
    hui

    hui

    hewies user interface - 3D scientific visualisation tool

    Python project with goal to provide FOSS library to extract, analyse and visualise data in a 3D fashion. The instance will connect to a data source, ods sheet, csv, sql DB, pyodbc the instance will analyse and/or transform the data to be presented to the visualisation functionality the instance will visualise the data in a 3D fashion, likely using third party FOSS
    Downloads: 0 This Week
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  • 4
    Reproducible-research

    Reproducible-research

    A Reproducible Data Analysis Workflow with R Markdown, Git, Make, etc.

    ...It combines the benefits of various open-source software tools including R Markdown, Git, Make, and Docker, whose interplay ensures seamless integration of version management, dynamic report generation conforming to various journal styles, and full cross-platform and long-term computational reproducibility. The workflow ensures meeting the primary goals that 1) the reporting of statistical results is consistent with the actual statistical results (dynamic report generation), 2) the analysis exactly reproduces at a later point in time even if the computing platform or software is changed (computational reproducibility), and 3) changes at any time (during development and post-publication) are tracked, tagged, and documented while earlier versions of both data and code remain accessible.
    Downloads: 0 This Week
    Last Update:
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